The Book of Alternative Data

The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management

Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject.

This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors--leading experts in financial modeling, machine learning, and quantitative research and analytics--employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book:

• Provides an integrated modeling approach to extract value from multiple types of datasets

• Treats the processes needed to make alternative data signals operational

• Helps investors and risk managers rethink how they engage with alternative datasets

• Features practical use case studies in many different financial markets and real-world techniques

• Describes how to avoid potential pitfalls and missteps in starting the alternative data journey

• Explains how to integrate information from different datasets to maximize informational value

The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.

Auteur

ALEXANDER DENEV is Head of AI, Financial Services - Risk Advisory at Deloitte LLP. Prior to that he led Quantitative Research & Advanced Analytics at IHS Markit. Previously, he held roles at the Royal Bank of Scotland, Societe Generale, and European Investment Bank. Denev is a visiting lecturer at the University of Oxford where he graduated with a degree in Mathematical Finance. He is author of numerous papers and books on novel methods of financial modeling with applications ranging from stress testing to asset allocation. SAEED AMEN is the founder of Cuemacro, where he consults on systematic trading. For 15 years, he has developed systematic trading strategies and quantitative indices including at major investment banks, Lehman Brothers and Nomura. He is also a visiting lecturer at Queen Mary University of London and a co-founder of the Thalesians, a quant think tank.

Texte du rabat
Praise for The Book of Alternative Data "Alternative data is one of the hottest topics in the investment management industry today. Whether it is used to forecast global economic growth in real-time, parse the entrails of a company with more granularity than that offered by a quarterly report, or to better understand stock market behavior, alternative data is something that everyone in asset management needs to get to grips with. Alexander Denev and Saeed Amen are able guides to a convoluted subject with many pitfalls, both technical and theoretical, even for those that still think Python is a snake best avoided."
Robin Wigglesworth, Global Finance Correspondent, Financial Times "Congratulations to the authors for producing such a timely, comprehensive, and accessible discussion of alternative data. As we move further into the 21st Century, this book will rapidly become the go-to work on the subject."
David Hand, Senior Research Investigator and Emeritus Professor of Mathematics, Imperial College London "Over the last decade, Alternative Data has become central to the quest for temporary monopoly of information. Yet, despite its frequent use, little has been written about the end-to-end pipeline necessary to extract value. This book fills the omission, providing not just practical overviews of machine learning methods and data sources, but placing as much importance on data ingestion, preparation, and pre-processing as on the models that map to outcomes. The authors do not consider methodology alone, but also provide insightful case studies, practical examples and highlight the importance of cost-benefit analysis throughout. For value extraction from Alternative Data, they provide informed insights and deep conceptual understandingcrucial if we are to successfully embed such technology at the heart of trading."
Stephen Roberts, Royal Academy of Engineering and Man Group Professor of Machine Learning, University of Oxford, UK; director, Oxford-Man Institute of Quantitative Finance "True investment outperformance comes from the triad of data + machine learning + supercomputing. Alexander Denev and Saeed Amen have written the first comprehensive exposition of alternative data, revealing sources of alpha that are not tapped by structured datasets. Asset managers unfamiliar with the contents of this book are not earning the fees they charge to investors."
Dr. Marcos López de Prado, Professor of Practice, Cornell University; CIO, True Positive Technologies LP "Alexander and Saeed have written an important book about an important topic. I am involved with alternative data every day, but I still enjoyed the perspectives in the book and learned a lot. I highly recommend it to everybody looking to harness the power of alt data (and avoid the pitfalls!)."
Jens Nordvig, Founder and CEO, Exante Data

Contenu
Preface xv

Acknowledgments xvii

Part 1 Introduction and Theory 1

1 Alternative Data: The Lay of the Land 3

1.1 Introduction 3

1.2 What is Alternative Data? 5

1.3 Segmentation of Alternative Data 7

1.4 The Many Vs of Big Data 9

1.5 Why Alternative Data? 11

1.6 Who is Using Alternative Data? 15

1.7 Capacity of a Strategy and Alternative Data 16

1.8 Alternative Data Dimensions 19

1.9 Who Are the Alternative Data Vendors? 23

1.10 Usage of Alternative Datasets on the Buy Side 24

1.11 Conclusion 26

2 The Value of Alternative Data 27

2.1 Introduction 27

2.2 The Decay of Investment Value 27

2.3 Data Markets 29

2.4 The Monetary Value of Data (Part I) 31

2.4.1 Cost Value 34

2.4.2 Market Value 34

2.4.3 Economic Value 35

2.5 Evaluating (Alternative) Data Strategies with and without Backtesting 35

2.5.1 Systematic Investors 36

2.5.2 Discretionary Investors 38

2.5.3 Risk Managers 39

2.6 The Monetary Value of Data (Part II) 39

2.6.1 The Buyer's Perspective 40

2.6.2 The Seller's Perspective 41

2.7 The Advantages of Maturing Alternative Datasets 45

2.8 Summary 46

3 Alternative Data Risks and Challenges 47

3.1 Legal Aspects of Data 47

3.2 Risks of Using Alternative Data 50

3.3 Challenges of Using Alternative Data 51

3.3.1 Entity Matching 52

3.3.2 Missing Data 54

3.3.3 Structuring the Data 55

3.3.4 Treatment of Outliers 56

3.4 Aggregating the Data 57

3.5 Summary 58

4 Machine Learning Techniques 59

4.1 Introduction 59

4.2 Machine Learning: Definitions and Techniques 60

4.2.1 Bias, Variance, and Noise 60

4.2.2 Cross-Validation 61

4.2.3 Introducing Machine Learning 62

4.2.4 Popular Supervised Machine Learning Techniques…